How well does 'R in a Nutshell' cover missing data?
I'm somebody who knows the basics of R, and is trying to use it on real data,
where there are things like missing values. I've just found out that running a lm
on a data frame with missing values results in residual and coefficient vectors with different lengths than the number of rows in the data frame, and spent a day thrashing around helplessly. I'd to avoid wasting time in the future.
Thanks for your interest in R in a Nutshell. I contacted one of the editors of this title who got a reply from the author concerning your question:
"Missing data is covered throughout the book; there isn't a single chapter on it. The example in the book are all based on real, dirty data, so there are definitely missing values in the data. For example, in the regression chapter, I identify which algorithms deal naturally with missing values (like recursive partitioning trees) and which ones require filtering or imputation (like multivariate adaptive regression splines)."
Hope that helps and have a great day!
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